There are a variety of types of imagers or cameras. Conventional digital cameras capture the spatial distribution of intensity and color in a scene, usually using a detector that includes a two dimensional array of pixels, such as a focal plane array (FPA), for example.
A “polarimetric imager” or “polarimeter” is a camera capable of viewing a scene in polarized light. The end result is detection and analysis of the polarization state of light reflected from an object, which is sensitive to the object orientation, composition, and surface roughness. Manmade objects tend to have highly polarized returns, whereas natural objects more often do not, and this has proven to be a useful discriminator for identifying hidden manmade objects, such as vehicles hidden in foliage, for example. The polarization in which the image is viewed is preferably selectable, and the spatial variations of the polarization state of an image are captured on a pixel-by-pixel basis.
Data collected from a polarimeter can be arranged as a three dimensional data cube of intensities for two special dimensions and, conceptually, one polarization dimension, I(x, y, p). In actuality the polarization state is a four dimensional vector, and the “p” axis comprises just four values. Intensities at each pixel in a two dimensional spatial grid may be recorded time sequentially for a sufficient number of polarization states to span the polarization space, such as the components of the four-dimensional Stokes vector that completely specifies a polarization field as unpolarized, partially polarized, or arbitrarily polarized. Polarimeters are inherently computational imagers. Data is collected (as the data cube) and processed by tomographic techniques to present images with polarization state contours. Once the data cube is known, an image in any arbitrary polarization can then be reconstructed. Alternatively, the same data can be post-processed to give the complex polarization factor for the light reflected off or emitted from the target scene, pixel by pixel.
Conventional polarimeters use multiple rotating polarization elements (polarizers, phase retarders, or filters) to map out the complete state of polarization for a scene through a time sequenced series of exposures and subsequent post processing. Such instruments are not fast enough to image moving targets or stationary targets from moving platforms.
Another type of polarimeter is known as a “snapshot” polarimeter, wherein all the polarization data for all of the image pixels is collected with a single exposure, and without the use of rotating or scanning components. On example of a snapshot polarimeter includes a fixed array of miniature polarizers affixed directly to a focal plane array. Three orientations of the polarizers are provided to sample the first three of the four Stokes vector components. Information on circular polarization is not collected. Four pixels of the FPA are used to represent a single pixel of the polarization image. More recently, there have been developments in polarimeters that can capture the full Stokes vector with a single measurement using spatial multiplexing and complex processing. Although complete polarization information may be acquired from a single exposure of the detector array, the limited bandwidth of the spatial modulation used to recover the polarization information may limit measurement accuracy, and the processing time required is substantial.
A spectral imager collects spatial and spectral data from a scene, with an object data cube comprising intensities for a range of wavelengths at each spatial coordinate, I(x, y, λ). Spectral information is obtained by dispersing the input light with gratings or prisms onto a detector array. Data collection generally requires scanning in at least one dimension, owing to the two dimensional nature of the focal plane array, and the three dimensional nature of the object data cube. Spectral imagers typically use a “push broom” data collection system, whereby the image is sampled by a slit and projected onto a prism, the prism dispersing the slit beam to cover the focal plane array. The spectra are collected as column vectors on the focal plane array, one column for each pixel of a single spatial dimension (x, λ). The process is repeated by moving the slit across the image, collecting one frame for each pixel of the second spatial row of dimension, y. The end result is a sequence of one-dimensional spectral images (x, λ), the number of images being equal to the focal plane array pixel count for the second dimension. Such a spectral imager is inherently a computational optics system; an image is not available until all the data have been collected and processed. Generally a two-dimensional image in any spectral component can be reconstructed from the data cube by computed tomography techniques. Such spectral imaging is widely used for remote sensing. Other scanning techniques used include whiskbroom, rotating filter wheels, and rotating prisms. Fourier transform imaging spectrometers mechanically scan the optical path length difference of an interferometer, thereby providing a spatial modulation that allows the polarization information to be separated. In each case scanning limits the data acquisition speed such that only static images can be reasonably collected from static platforms.
As with polarimeters, recent developments in spectral imagers have produced single snapshot (non-scanning) spectral imagers, whereby the entire data cube is collected in a single exposure, with no moving parts. One example of a snapshot spectral imager is the computer tomography imaging spectrometer (CTIS). The CTIS uses a computer-generated hologram as the disperser, and tomographic processing is used to recover the spectrum at each image pixel. The processing time for CTIS instruments is still relatively long, and there is a need for an oversized FPA to collect all of the diffraction orders simultaneously. Furthermore, image reconstitution suffers from incomplete sampling of the data cube that is inherent to the limited range of diffraction angles (the missing cone problem).
A polarimetric spectral imager is a relatively new class of imagers that can collect spectral data from a scene as viewed in any arbitrary polarization. The data cube of a polarimetric spectral imager is a four-dimensional volume comprising two spatial coordinates, wavelength, and polarization p, usually represented by the Stokes vector components: I(x,y,λ,p). Conventionally, the so-called spectropolarimetric hypercube has been measured by consecutively scanning wavelength for each of the four Stokes vector components, which is a time consuming operation.